Character based media analytics

ABSTRACT

Techniques for analyzing media content are described. One technique generally comprises performing a regression analysis for characters in a plurality of media content based on user demographics, content outcome measure, and character models. The technique determines an attribute of significance. In some embodiments, the technique selects media content for display that depicts a character having at least a threshold value of the attribute of significance. In some embodiments, the technique displays media analytics for the attribute of significance determined based on a value of the attribute of significance exceeding a threshold significance value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation-In-Part of U.S. applicationSer. No. 14/466,882, filed on Aug. 22, 2014, which is a Continuation ofU.S. application Ser. No. 14/065,332, filed on Oct. 28, 2013 and issuedas U.S. Pat. No. 8,819,031 on Aug. 26, 2014, which is a Continuation ofU.S. application Ser. No. 13/844,125, filed on Mar. 15, 2013 and issuedas U.S. Pat. No. 8,572,097 on Oct. 29, 2013, the contents of all ofwhich are incorporated herein by reference in their entirety. Thepresent application also claims priority to U.S. Provisional ApplicationSer. No. 61/947,990, filed on Mar. 4, 2014, the contents of which areincorporated herein by reference in its entirety.

BACKGROUND

1. Field

The present disclosure relates generally to the field of character-basedmedia analytics and, more particularly, to character-based mediaanalytics using character decompositions.

2. Related Art

As media such as television shows and movies have become more ubiquitousand easily accessible in the everyday lives of consumers, the quantityand diversity of the media have also significantly increased.Previously, consumers were limited to a few television channelsbroadcasted by major television networks. As technology has progressed,various media are available for on-demand viewing at the convenience ofconsumers. As this on-demand ability has become more prevalent in thetelevision industry (e.g., on-demand movies) and the personal computingindustry (e.g., YouTube video streaming, NetFlix movie recommendations),consumers have become overwhelmed with the availability of choices atany one time. Similarly, consumers' ability to search through media todiscover new content that meets their personal preferences and tasteshas remained inefficient and ineffective.

Traditional techniques for discovering new media rely on friends andacquaintances suggesting media that they believe the consumer may enjoy.Alternatively, the consumer may see a preview for media that capturestheir attention or the consumer may view media because it includes afavorite actor or actress. However, these techniques have a significantdrawback in that they use only a very narrow degree of precision inidentifying content and can be unreliable. For example, although afavorite actress may play the role of an educated, humble, andempowering individual in one movie, the same actress may play the roleof an illiterate, ill-mannered, and unfavorable individual in asubsequent movie. Therefore, understanding the qualities of charactersis helpful for appreciating and understanding the media in which thecharacters appear.

Accordingly, techniques for efficiently and reliably decomposing theattributes of characters for character-based media analytics areadvantageous.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates character models in vector space for multiplecharacters.

FIG. 2 illustrates an exemplary block diagram for performing discoveryand organization of characters and media content.

FIG. 3 illustrates an exemplary process for recommending media.

FIG. 4 illustrates another exemplary process for recommending media.

FIG. 5 illustrates an exemplary computing system.

FIG. 6 illustrates exemplary predictor data and outcome measures.

FIG. 7 illustrates an exemplary process for generating and usingcharacter-based insights and selecting media content for display.

FIG. 8 illustrates an exemplary process in accordance with someembodiments.

BRIEF SUMMARY

Systems and processes for analyzing media content is described. Thetechnique generally comprising: accessing demographics information of aplurality of users to identify a subset of the plurality of users;accessing outcome measure information of the subset of the plurality ofusers, the outcome measure information relating to a plurality of mediacontent, the plurality of media content comprising a first media contentand a second media content; calculating a first outcome measure for thefirst media content, the first outcome measure based on the outcomemeasure information; calculating a second outcome measure for the secondmedia content, the second outcome measure based on the outcome measureinformation; accessing respective character models of one or morecharacters depicted in the first media content; accessing respectivecharacter models of one or more characters depicted in the second mediacontent; determining, for the first media content: a first attributevalue of a first attribute of the one or more characters depicted in thefirst media content, the determination based on the respective charactermodels and in accordance with a first capture function; and a secondattribute value of a second attribute of the one or more charactersdepicted in the first media content, the determination based on therespective character models and in accordance with a second capturefunction; determining, for the second media content: a third attributevalue of the first attribute of the one or more characters depicted inthe second media content, the determination based on the respectivecharacter models and in accordance with the capture function; and afourth attribute value of the second attribute of the one or morecharacters depicted in the second media content, the determination basedon the respective character models and in accordance with the capturefunction; and performing a regression using the first attribute value,the second attribute value, the third attribute value, the fourthattribute value, the first outcome measure, and the second outcomemeasure to determine at least one attribute of significance.

DETAILED DESCRIPTION

The following description is presented to enable a person of ordinaryskill in the art to make and use the various embodiments. Descriptionsof specific devices, techniques, and applications are provided only asexamples. Various modifications to the examples described herein will bereadily apparent to those of ordinary skill in the art, and the generalprinciples defined herein may be applied to other examples andapplications without departing from the spirit and scope of the variousembodiments. Thus, the various embodiments are not intended to belimited to the examples described herein and shown, but are to beaccorded the broadest scope consistent with the claims.

The embodiments described herein include technologies directed toenabling the organization and discovery of characters and media contentbased on the characters (or attributes of the characters) present in themedia. Further, the embodiments described herein include technologiesdirected to enabling character-based analytics for media content. Mediaand media content refer to content for storing or deliveringinformation. For example, media content may include television shows,movies, YouTube videos, digital streaming Internet videos, books, poems,stories, audio files, advertisements, news, articles, images, and thelike.

A character refers to a persona. For example, characters may includepoliticians, actors/actresses, real-world persons, book characters,narrators, and anthropomorphized entities within the content beinganalyzed, and the like. Attributes of characters refer to qualities ofthe characters. For example, the character's career (e.g., scientist,lawyer, doctor, secretary), demographic (e.g., age, gender, race,parental status), location (e.g., urban, rural), social traits (e.g.,nice, loyal, funny, leader, popular, friendly), physical traits (e.g.,tall, short, weight, attractiveness), intellectual traits (e.g.,competent at a particular task, intelligent, hardworking, good at math),life traits (e.g., underdog, spoiled), and the like are attributes ofcharacters. Attributes may be represented in a binary space, such asdifferentiating between a character being “nice” or “not nice.”Attributes may also be represented in a continuous space, such asdifferentiating the degree to which a character is nice on a real numberscale of 0 to 10, −10 to 10, 0 to 100, or the like.

When considering longer form content such as television shows and moviesthe character attributes that can be reliably assigned to a charactermay be of a more persistent and sustained nature (e.g. attractiveness,leadership, or whether the characters are respected). While these traitsare by no means fixed, they change over long periods of time so thatthat values of an attribute (e.g., attractiveness) may be assigned to acharacter in a movie or even a long running show.

For shorter form media content, such as some content found in onlinevideo platforms, the space of attributes that can be reliably andspecifically attributed to characters in that content expandsconsiderably to include those attributes that persist over much shorterperiods of time. Specifically, assignable attributes can be expanded tofurther include emotional and motivational states referenced above. Forexample, a popular video clip may be feature a person being outragedabout some injustice. This more transient state of outrage is a salientand important feature of the character that can be used for bothcuration and analytics in a similar way as the more persistentattributes of the character for short form content, while contextualizedin a longer piece of content, like a movie, it would be lessspecifically salient as a feature for either analytics or curation.

A set of illustrative, though not exhaustive, examples of emotionalstates that may be used for both curation and analytics of short-formvideo content includes: anger, outrage, excitement, elation, and pride.In addition to emotional states, motivational states of the charactersmay be used, such as what the character is pursuing in the momentillustrated by the media content. In many cases, these motivations aretied to the more persistent attributes of the characters. For example,characters may be in pursuit of being attractive, intelligent,knowledgeable, likeable, wealthy, or nurturing. Continuing the aboveexample, the outrage expressed may be paired with some action to repairthe injustice, displaying a motivation to improve the world and one'scommunity. In other characters, the motivations may actually be inpursuit of more short term needs and desires, such as satisfying hungeror thirst.

A consumer may enjoy a particular television show because of thepositive message portrayed by a character in the show. This positivemessage is often based on multiple attributes of the characters in theshow, rather than strictly the characters' actions or the content of thecharacter's verbal speech. For example, attractive female charactersthat are depicted as confident and intelligent portray a positivemessage. To understand why the consumer is attracted to a character, itis helpful to build a character model that captures the character'sattributes. A character preference function based on the consumer'spreferred attributes may also be developed by either directly elicitingwhy a particular consumer likes or relates to characters, or simply byinferring a preference function based on a set of preferred and/ornon-preferred characters. Character models and character preferencefunctions are used to recommend media to a consumer, rate the likelihoodthat a consumer will enjoy or respond to particular piece of content,recommend new characters to a consumer, recommend or deliver othercontent to the consumer, or rate the likelihood that a consumer willenjoy a particular character or piece of content.

Character Models in Vector Space

FIG. 1 illustrates character models in vector space for multiplecharacters. In FIG. 1, character models 116-128 are mapped out based onthe attributes of the characters 102-114 from the television show TheBig Bang Theory. The Big Bang Theory television show is a sitcom thatbrings together a wide array of characters ranging from geeky,intellectual characters with limited social skills to characters thatare less educated, but socially adept. Each character model illustratedin FIG. 1 captures a multidimensional representation of the attributesof an associated character. In this example, each of the charactermodels 116-128 is stored in the form of a vector. One of ordinary skillin the art will appreciate that the character models may be stored usingvarious methodologies.

In the example of FIG. 1, the vectors for character models 116-128 arefour-dimensional. The value in the first dimension of each charactermodel is based on the character's gender, with female represented by 1and male represented by a −1. The value in the second dimension of eachcharacter model is based on whether the character is a scientist, withscientist represented by 1 and non-scientist represented by −1. Thevalue in the third dimension of each character model is based on theattractiveness of the character, with −1 representing unattractive, 0representing neutral attractive, and 1 representing attractive. Thevalue in the fourth dimension of each character model is based onwhether the character is friendly, with 1 representing friendly and −1representing unfriendly.

In The Big Bang Theory sitcom, Sheldon 102 is a male theoreticalphysicist researching quantum mechanics and string theory. Sheldon hasB.S., M.S., M.A., Ph.D., and Sc.D. degrees. He is an unfriendlyintrovert who is physically unattractive. Sheldon's 102 attributes aremapped to vector 116. Vector 116 is (−1, 1, −1, −1). Vector 116 isannotated for clarity as (−1 [male], 1 [scientist], −1 [unattractive],−1 [unfriendly]).

Leonard 104 is a male physicist on The Big Bang Theory. He received hisPh.D. at the age of 24. His physical attractiveness is neutral, meaninghe is neither attractive nor unattractive, and he is friendly. Leonard's104 attributes are mapped to vector 118. Vector 118 is (−1, 1, 0, 1).Vector 118 is annotated for clarity as (−1 [male], 1 [scientist], 0[neutral attractive], 1 [friendly]).

Penny 106 is a friendly, attractive, tall, blonde, female waitress whois pursuing a career in acting. Penny's 106 attributes are mapped tovector 120. Vector 120 is (1, −1, 1, 1). Vector 120 is annotated forclarity as (1 [female], −1 [nonscientist], 1 [attractive], 1[friendly]).

Howard 108 is a male aerospace engineer and has an M.Eng. degree. He issocially outgoing and friendly but is physically unattractive. Howard's108 attributes are mapped to vector 122. Vector 122 is (−1, 1, −1, 1).Vector 122 is annotated for clarity as (−1 [male], 1 [scientist], −1[unattractive], 1 [friendly]).

Rajesh 110 is a male particle astrophysicist at Caltech and has a Ph.D.His physical attractiveness is neutral and he is friendly. Rajesh's 110attributes are mapped to vector 124. Vector 124 is (−1, 1, 0, 1). Vector124 is annotated for clarity as (−1 [male], 1 [scientist], 0 [neutralattractive], 1 [friendly]).

Bernadette 112 is a female with a Ph.D. in microbiology. She is bothattractive and friendly. Bernadette's 112 attributes are mapped tovector 126. Vector 126 is (1, 1, 1, 1). Vector 126 is annotated forclarity as (1 [female], 1 [scientist], 1 [attractive], 1 [friendly]).

Amy 114 is a female who has a Ph.D. in neurobiology. She is unfriendlyand physically unattractive. Amy's 114 attributes are mapped to vector128. Vector 128 is (1, 1, −1, −1). Vector 128 is annotated for clarityas (1 [female], 1 [scientist], −1 [unattractive], −1 [unfriendly]).

Vector Space Driven Searches

Character models described in vector space may be used for varioussearches. In one example, character models 116-128 of FIG. 1 may be usedto quickly and accurately identify all characters that exhibit aparticular attribute. To identify all characters that are attractive, asearch is conducted where an equality test is performed on the thirdelement of the character model vectors. As discussed above and describedin the example of FIG. 1, the third dimension (or third value) of eachvector indicates the attractiveness of the associated character. Allcharacter models with a value greater than zero as the third dimensionof the vector are associated with an attractive character. In theexample of FIG. 1, Penny and Bernadette are quickly and accuratelyidentified as being attractive by determining that they have anattractiveness value that is greater than 0.

Similarly, Sheldon, Howard, and Amy can be quickly and accuratelyidentified as unattractive as they have an attractiveness value that isless than 0, indicating they are unattractive. As discussed above, thesecond dimension (or second value) of the character models 116-128describe whether the character is a scientist or nonscientist. A searchfor all scientists would identify all character models with a value of 1in the second dimension. In the example illustrated in FIG. 1, a searchfor scientists returns results for Sheldon, Leonard, Howard, Rajesh,Bernadette, and Amy—everyone except Penny.

Additionally, a search for a particular characteristic of a charactermay depend on multiple dimensions of the character model vector. Forexample, a search for a “scientist” may be conducted by identifyingcharacters with character models that identify them as both “likesscience” and “good at science.”

Vector space may also be used to determine the distance betweencharacters. This distance is representative of how related (similar ordissimilar) two characters are to each other. Several techniques may beemployed to determine the distance between two characters.

Using a first technique, the distance d between a first character {rightarrow over (x)} associated with a first character model vector (x₁, x₂,x₃, x₄) and a second character {right arrow over (y)} associated with asecond character model vector (y₁, y₂, y₃, y₄) can be determined usingthe weighted Euclidean distance:

${d\left( {\overset{\rightarrow}{x},\overset{\rightarrow}{y}} \right)} = \sqrt{{\beta_{1}\left( {x_{1} - y_{1}} \right)}^{2} + {\beta_{2}\left( {x_{2} - y_{2}} \right)}^{2} + {\beta_{3}\left( {x_{3} - y_{3}} \right)}^{2} + {\beta_{4}\left( {x_{4} - y_{4}} \right)}^{2}}$

More generally, the weighted Euclidean distance d between a firstcharacter {right arrow over (x)} and a second character {right arrowover (y)} for an N-dimensional space can be calculated using thefollowing equation:

d({right arrow over (x)},{right arrow over (y)})=√{square root over(Σ_(i=1) ^(n)β_(i)(x _(i) −y _(i))²)}

As an example of this first technique, the distance between Sheldon andLeonard can be computed using the character models 116 and 118 of FIG. 1as the following, assuming the weights β_(i)=1 for all i:

${d\left( {{Sheldon},{Leonard}} \right)} = {\sqrt{\left( {\left( {- 1} \right) - \left( {- 1} \right)} \right)^{2} + \left( {1 - 1} \right)^{2} + \left( {\left( {- 1} \right) - 0} \right)^{2} + \left( {\left( {- 1} \right) - 1} \right)^{2}} = \sqrt{5}}$

As illustrated by this calculation, elements of the character modelsthat have the same value do not contribute to the distance. Thus, if twocharacters have identical character models, their distance will be 0. Inthe case of Sheldon and Leonard, they share many, but not all,attributes. In particular, the differences between Sheldon and Leonardare their attractiveness and their friendliness. The squared differencein friendliness has a larger contribution (i.e., 4) than thecontribution (i.e., 1) resulting from the squared difference inattractiveness. As a result, the distance between the two characters isthe square root of 5.

Using a second technique, the distance d between a first character{right arrow over (x)} associated with a first character model vector(x₁, x₂, x₃, x₄) and a second character {right arrow over (y)}associated with a second character model vector (y₁, y₂, y₃, y₄) can bedetermined by performing a comparison of values of the character models:

d({right arrow over (x)},{right arrow over (y)})=(x ₁ !=y ₁)+(x ₂ !=y₂)+(x ₃ !=y ₃)+(x ₄ !=y ₄)

In this comparison, the result of two compared values is 1 when they arenot equal. Similarly, the result of two compared values is 0 when theyare equal. If x₁ and y₁ are not equal, the value of (x₁ !=y₁) will be 1.This will contribute a value of 1 to the distance d({right arrow over(x)},{right arrow over (y)}). Alternatively, if x₁ and y₁ are equal, thevalue of (x₁ !=y₁) will be 0. This will not contribute to the distanced({right arrow over (x)},{right arrow over (y)}). Accordingly, distanceis less for characters using this second technique when the charactersshare attributes. This type of function is generally useful forattributes that can take on multiple values, but are not obviouslyorderable—such as race or hair color.

Once again, it might be true that certain attributes are more importanteither in general, or to a specific user than others. This can onceagain be represented by a set of “weights” β_(i). More generally, thedistance d between a first character {right arrow over (x)} and a secondcharacter {right arrow over (y)} for an N-dimensional space can becalculated using the following equation:

${d\left( {\overset{\rightarrow}{x},\overset{\rightarrow}{y}} \right)} = {\sum\limits_{i = 1}^{n}\; {\beta_{i}\left( {x_{i}!=y_{i}} \right)}}$

As an example of this second technique, the distance between Sheldon andLeonard can be computed using the character models 116 and 118 of FIG. 1as the following:

d(Sheldon,Leonard)=((−1)!=(−1))+(1!=1)+((−1)!=0)+((−1)!=1)=2

As illustrated by this calculation, elements of the character modelsthat have the same value do not contribute to the distance. Thus, if twocharacters have identical character models, their distance will be 0. Inthe case of Sheldon and Leonard, they share many, but not all,attributes. In particular, the differences between Howard and Rajesh areattractiveness and friendliness. The two differences each contribute thesame amount to the distance (i.e., 1). As a result, the distance betweenthe two characters is 2.

Both of these techniques use simple, symmetric functions often used tocompute distances in vector spaces. However, in the case of charactersit may be true that when computing the distance from a first character{right arrow over (x)} to a second character {right arrow over (y)} youmay consider attributes “important” to character {right arrow over (x)}more important, while when computing the converse distance fromcharacter {right arrow over (y)} to character {right arrow over (x)} youwould consider attributes “important” to character {right arrow over(y)}. For example—we might decide that whenever a character was“neutral” on a particular attribute, the “weight” on that attribute is0, and otherwise the “weight” on that attribute should be one. In thiscase the distance from Sheldon to Leonard:

${d\left( {{Sheldon},{Leonard}} \right)} = {\sqrt{\left( {{1\left( {{- {1--}}1} \right)^{2}} + {1\left( {{- {1--}}1} \right)^{2}} + {1\left( {{- 1} - 0} \right)^{2}} + {1\left( {{- 1} - 1} \right)^{2}}} \right.} = \sqrt{5}}$

However,

${d\left( {{Sheldon},{Leonard}} \right)} = {\sqrt{\left( {{1\left( {{- {1--}}1} \right)^{2}} + {1\left( {{- {1--}}1} \right)^{2}} + {0\left( {{0--}1} \right)^{2}} + {1\left( {{1--}1} \right)^{2}}} \right.} = 2}$

Thus, the distance from Sheldon to Leonard is greater than the distancefrom Leonard to Sheldon because “appearance” is more salient toSheldon's character than Leonard's. There are many other ways in whichthese distance functions might be complicated to accommodate features ofthe character space, or of the specific user.

In full generality, any function taking two elements in the characterspace to a scalar could be used as a distance function. For distance d:

d:(

^(n)×

^(n))→

In another example, both the first and second techniques for determiningdistance between two characters will result in larger distances betweenPenny and Sheldon than were computed for Sheldon and Leonard. Thedistances between Penny and Sheldon will be at their maximum for the twotechniques because Penny and Sheldon are exact opposites on all fourdimensions of their character models.

As discussed above, the distance between characters represents thedegree of similarity between the characters. Thus, when it is known thata consumer likes a particular character, a computing system canrecommend additional characters that have a relatively low distance fromthe known character. The system may recommend all known characters thathave a distance from the known character that is below a certainthreshold. Alternatively, or in addition, the system may recommend Xnumber of closest characters, where X is a threshold set by a user ordetermined by the system. Alternatively, or in addition, the system mayrecommend a ranked list based on level of relevancy or distance.

Developing Character Models

The character models 116-128 illustrated in FIG. 1 provide an examplefor a single television show. To develop character models for numerouscharacters spanning a large variety of media content, automatedtechniques, partially automated techniques, manual techniques, and theircombinations are employed. Several techniques are discussed below, whichmay be used independently or in combination.

Semantic analysis of text may be used to develop character models. Textassociated with a character is identified across different text-basedmedia, such as Internet websites. Terms associated with the characterare aggregated from the text. Semantic analysis techniques are then usedto map the character onto the desired feature space. For example, acharacter model for Penny 106 may be developed using semantic analysisby identifying text associated with Penny 106. For example, text may beidentified with a character when it is a certain number of words or lessaway from the character's name or image. Various terms, such as“engineer,” “science,” or “analytical,” are aggregated from theidentified text. These terms are mapped to the appropriate attribute ofthe character. In this case, the appropriate attribute is “scientist.”In one example, each time a term maps to an attribute of the character,that character's attribute value increases by a determined amount—suchas one. Similarly, when a term maps to the negative of an attribute ofthe character, such as “dislikes math,” that character's attribute valuedecreases by a determined amount—such as one. In either case, thedetermined amount for increasing or decreasing the attribute value maybe based on a strength value of the term. The term “engineer” may have astrength value of 0.25 while the term “excited” has a strength value of1.0. Similarly, “incredibly excited” may have a strength value of 1.5.The mapping and strength values may be stored in a database for easyaccess when developing the character models.

Potential sources of the terms that describe the character include thecharacter's official webpage, Wikipedia pages for the character and showthe character appears in, fan pages, social networking pages, socialnetworking chatter (e.g., tweets from Twitter, Facebook comments, etc.),and other Internet sources.

Aggregating users' responses to a character may also be used to developcharacter models. For example, responses related to a character'sattribute may be determined as “positive” or “negative” and used toincrease or decrease the attribute value in the character modelaccordingly. For example, users may reference a character as being“smart,” which increases the attribute value for intelligence, or as“dimwitted,” which decreases the attribute value for intelligence.Users' responses may be aggregated from across the Internet, such associal networks, webpages, emails, and the like. Additionally, charactermodels may be based on explicit thumbs up and down or Likert ratings(e.g., using the Likert scale) by users, clustering user preferences forcharacters with other web pages the user likes and/or Internet groups ofwhich the user is a part. These models can be additionally based on theexpertise of web pages and Internet groups that mention the character,awards, trade magazines, expert commentary, and editorial reviews.

Survey methodologies may be used to develop character models. A surveycan be conducted to assess a population's opinion about a character'sattributes. The surveys may ask several questions to get the underlyingvalue for a more subtle attribute. For example, to assess a “socialcompetence” attribute, respondents may be asked if the character has alot of friends, if the character is familiar with popular culture, andif the character is able to adapt to both formal and informalsituations.

These surveys may be, for example, full-length surveys looking at eachrespondent's overall reaction to a character or micro-surveys askingrespondents single, discrete questions using services such as MechanicalTurk or in house surveys shown along with the content being assessed.

Expert validation may be used to develop character models. Certainattributes, such as “agency” or “moral character,” may benefit frominput from experts in various fields including media studies andpsychology. For these attributes, survey methodologies may be combinedto populate the majority of the database, with expert validation on arandomly selected subset to ensure methodologies used to populate themajority of the database are in line with best practices from thosefields.

User feedback may be used to develop character models. Users' responsesto characters may be aggregated and used to feed into the database ofcharacter models. For example, when a consumer endorses via socialnetworks, shares with friends, or watches a given character in a mediacontent, the consumer is prompted to provide feedback on why they liked,shared, or viewed that particular character or media content.

Character Preference Function

Thus far, the described techniques for determining distance have notdifferentiated between the importance of the various characterattributes as viewed from the perspective of a consumer. To be moreprecise, we have defined a single distance function applicable to anycharacter. These search techniques can be further refined by taking intoaccount whether a consumer cares more about similarity along somedimensions of the character model than other dimensions of the charactermodel. This preference information about the consumer is captured in acharacter preference function and is used for determining preferencesand distances between characters.

Different consumers may have different character preference functions,which are each based on the associated consumer's preferences. Forexample, Jessica, a television viewer, may care only about the gender ofcharacters and the attractiveness of characters. In particular, shelikes attractive characters and female characters. These preferences maybe gathered directly or indirectly. For example, a user may directlyinput their preferences or the user's preferences may be learned byidentifying which characters the user likes. As alluded to above, theseuser-specific preferences can be encoded in a set of “weights” β_(i) foreach attribute. Here, Jessica's character preference function isrepresented as:

ƒ(Jessica)=β₁ ·c ₁+0·c ₂+β₃ ·c ₃+0·c ₄

where β₁, β₃ are both greater than 0. In this example, the coefficientson the second and fourth attributes (i.e., coefficient to c₂ scientistattribute and coefficient to c₄ friendliness attribute) are 0 becauseJessica does not care about them, and the coefficients on the attributesshe likes (i.e., β₁ coefficient to c₁ gender attribute and β₃coefficient to c₃ attractiveness attribute) are positive. If Jessicapreferred male characters rather than female characters, the β₁coefficient for the gender attribute would be negative. Thesecoefficients may be referred to as the consumer's preferencecoefficients and they correlate to all or some of the values of thecharacter models. The preference coefficients may be integers or realnumbers. Negative preferences (e.g., a dislike for an attribute) may beincorporated into a preference function as well. One of ordinary skillin the art will appreciate that coefficients are a type of parameter,and that more generalized parameters for other functional forms may beused instead of coefficients in a linear function.

In another example, George, another television viewer, is interestedonly in the scientist dimension of characters. George likes scientistsregardless of their other attributes. George's character preferencefunction is significantly simpler than Jessica's character preferencefunction because George only cares about one dimension—the scientistdimension. Thus, the coefficients, or weights, on all the otherdimensions are 0. George's character preference function is reduced to:

ƒ(george)=β₂ ·c ₂

where β₂ is greater than 0. Jessica's and George's preferences arecaptured in their character preference functions. These characterpreference functions can be used to recommend characters and todetermine distances between characters, with both recommendations anddistances being individualized for the consumer associated with thecharacter preference function.

As discussed above, the system can recommend characters based on thecharacter preference function. Using Jessica's character preferencefunction and the character models illustrated in FIG. 1, the system willrank Penny 106 and Bernadette 112 highly. This system will recommendPenny 106 and Bernadette 112 to Jessica because Penny 106 and Bernadette112 are ranked highly based on a combination of Penny's character model120, Bernadette's character model 126, and Jessica's characterpreference function. The recommendation values that are translated intorankings can be calculated using the consumer's character preferencefunction:

ƒ(consumer,character)={right arrow over (β)}·{right arrow over (c)}

where β represents the preference coefficients of the consumer along nattributes and c represents a character's attributes, such as from acharacter model, along the same n attributes. This character preferencefunction may be computed multiple times for different characters todetermine the distance between characters for the particular consumer.

Based on George's character preference function and the character modelsillustrated in FIG. 1, the system will rank all characters that arescientists equally highly and recommend them to George. Similarequations can be used to calculate recommendation values for George.

The character preference function may also represent different oradditional information than information capturing what the consumerlikes or dislikes. For example, Jessica's character preference functionmay represent what Jessica likes, what type of characters or contentJessica has viewed in the past, what character or content Jessica hasprovided feedback on, whether the feedback has been positive/negative, acombination of one or more of these elements, or the like.

The system can also determine distances between characters by using thecharacter preference functions in combination with the character models.For example, based on Jessica's character preference function describedabove and the character models illustrated in FIG. 1, the system willidentify Penny and Bernadette as having a relatively low distancebecause Penny and Bernadette share the same gender (female) andattractiveness (attractive). Recall that gender and attractiveness arethe two dimensions that are relevant in Jessica's character preferencefunction. If gender and attractiveness are the only dimensions relevantto Jessica's character preference function, the distance between Pennyand Bernadette is 0. Similarly, the system will determine that Howardand Penny have a relatively high distance because Howard and Pennydiffer in both gender and attractiveness. In particular, Howard is aunattractive and male while Penny is attractive and female. Again,recall that Jessica's character preference function emphasizes thegender and attractiveness dimensions. Thus, Howard and Penny may beviewed by the system as opposites with respect to Jessica's characterpreference function because Howard and Penny differ in both gender andattractiveness.

Second Order Terms for Character Preference Functions

In some instances, the distances between characters based on charactermodels and character preference functions may be computed using secondorder or higher terms. For example, a consumer may like male scientistsbut may dislike female scientists. Similarly, a consumer may likeattractive females as well as attractive scientists. To distinguishamong these combinations, second order or higher preferences need to becaptured in the character preference function. With regard to secondorder terms, note that, for example, a preference for an attractivefemale character (second order) is different than a preference for bothattractive characters (first order) and female characters (first order).

In one example, a consumer named Brian likes female scientist and malenon-scientist characters (second order), and attractive characters(first order), and friendly characters (first order). Note that apreference for a female scientist is different than a preference forfemale characters and characters that are scientists. Brian's characterpreference is:

ƒ(c)=β₁ ·c ₁+β₂ ·c ₂+β₃ ·c ₃+β₄ ·c ₄+γ_(1,2) ·c ₁ ·c ₂

where β₁, β₂, β₃, β₄, γ_(1,2), γ_(3,4)>0. The second order term capturedby the positive coefficient γ_(1,2), γ_(3,4) provide more precisemetrics for character recommendations and distances between characterswith relation to Brian's preferences. Brian's character preference isdescribed below with annotations for clarity:

ƒ(c)=β₁ ·c ₁[gender]+β₂ ·c ₂[scientist]+β₃ ·c ₃[attractiveness]+β₄ ·c₄[friendliness]+γ_(1,2) ·c ₁[gender]·c ₂[scientist]

This second order of preference is captured in Brian's characterpreference function to provide more precise metrics for characterrecommendations and distances between characters with relation toBrian's preferences.

In particular, Brian's character preference function illustrates thatBrian likes female scientists. This is stored in Brian's characterpreference function using the vector of weights {right arrow over (β)}and the matrix of weights for the second order terms γ. Thus, whencomputing recommendations and distances using Brian's characterpreference function, the system can take Brian's second orderpreferences into consideration. In this example with relation to Brian'scharacter preference function, the distance d between a first characterA and a second character B is determined as follows:

${d\left( {A,B} \right)} = {{\beta \cdot {{abs}\left( {A - B} \right)}^{\prime}} + {{{abs}\left( {A - B} \right)}^{\prime} \cdot \frac{\gamma}{2} \cdot {{abs}\left( {A - B} \right)}}}$

In some cases it may makes sense to split the character attributes intotheir positive and negative halves. For example, c_(i+)=c_(i) ifc_(i)>0, and c_(i+)=0 otherwise, and with c_(i−)=c_(i) if c_(i)<0, andc_(i)=0 otherwise. Doing this allows for more complicated second orderpreference. For example, if Brian liked female scientists, but didn'tparticularly care whether male characters were scientists or not, hecould instead have a preference function:

ƒ(c)=β₁ ·c ₁+β₂ ·c ₂+β₃ ·c ₃+β₄ ·c ₄+γ_(1+,2+) ·c ₁₊ ·c ₂₊

One of ordinary skill will readily appreciate that additional techniquesmay be used to represent the character preference functions.

Preference Models

A preference model may be developed by using data from multiplecharacter preferences function in conjunction with known attributesabout the users associated with the character preference functions. Adatabase of users is aggregated that associates users with one or moreattributes and their character preference function. Using this database,a preference model can be determined for a person or a group of people.

For example, assume that 75% of users who are both female and have adegree in a science, technology, engineering, or mathematics (STEM)field have user profiles that indicate they enjoy watching femalescientists in media. This is a strong indicator that other females witha degree in a STEM field will also enjoy watching female scientists inmedia. Thus, when a new user joins the system who provides their genderas female and education as related to STEM, the system can predict thatthe new user will enjoy watching female scientists without requiringdirect feedback from the new user about her viewing preferences.Accordingly, the system can recommend media using the techniquesdescribed above by using the prediction that the new user enjoyswatching media that includes female scientists.

Similarly, multiple character preference functions can be used topredict what characteristics a particular demographic will enjoy. Forexample, if a group viewing is being conducted (such as in a movietheater), statistics about the attributes of the group members can begathered in advance. The statistics about the group's attributes can beused to identify the types of characters the group is likely to enjoy.Using the techniques described above, media can be identified that thegroup is likely to enjoy. The preference model can also be extendedbeyond media to any type of character.

Beyond relying on demographic information, user specific preferencefunctions can be calculated in a number of different ways: 1) Directelicitation: asking users about their preferences for specificcharacters, character attributes, or combinations of attributes. 2)Inference from favorite characters and shows. For example, given a setof characters that the user likes and a set that the user does not like,one could estimate the preference weights by assuming that theprobability that the user “liked” a character was a sigmoid function

$\left( {{e.g.},\frac{1}{1 + {\exp \left( {- x} \right)}}} \right)$

of the user's character preference function. By finding the coefficientsβ, γ, that maximized the joint probability that the user likes and didnot like those sets of characters, the system can calculate the user'scharacter preference function. 3) Inference from physiologicalrecording: In the absence of direct reporting from the consumer abouttheir character preferences, eye-tracking, facial responses, posturemapping or any number of other types of physiological recording may beused to detect which characters demand the most attention from aconsumer, and whether that attention is positive or negative. Giventhese physiological responses to characters, a preference model could beinferred following a similar method as that described for 2). Thepreference model can also be extended beyond media to any type ofcharacter.

Content Recommendation

Information about characters can be used to determine ratings andrecommendations of media content and to determine distances betweenmedia content. For example, a consumer who likes attractive scientistswould likely enjoy a show that employs multiple characters that areattractive scientists. The same consumer would likely not enjoy a showthat primarily employs characters that are unattractive non-scientists.

Media content may be rated using a salience weighted sum of a consumer'spreferences for all or some characters included in the media content.The relative salience of the character in media content can bedetermined multiple ways.

One method to determine salience is to base the salience on thepercentage of screen time the character gets in relation to the totalscreen time of all characters. For a simple example, consider a comedyshow that includes a doctor, an engineer, and an attorney as characters.The doctor is on screen for a total of 1,100 seconds, the engineer is onscreen for a total of 1,500 seconds, and the attorney is on screen foronly 600 seconds. Using this first method, the salience S of a character(Char) in relation to all the characters (AllChars) can be computed as:

${S({Char})} = \frac{{ScreenTime}({Char})}{{ScreenTime}({AllChars})}$

In this particular example of the doctor, engineer, and attorney, thesalience for each character is computed as follows:

${S({doctor})} = {\frac{1,100}{{1,100} + {1,500} + 600} = 0.34375}$${S({engineer})} = {\frac{1,500}{{1,100} + {1,500} + 600} = 0.46875}$${S({attorney})} = {\frac{600}{{1,100} + {1,500} + 600} = 0.1875}$

Another method to determine salience is to base the salience on thenumber of reactions detected in social media relating to a character.For example, Twitter, Facebook, Google+, Instagram, and other socialnetworking websites may be monitored to track the number of times acharacter's name is mentioned, a character's image is published, acharacter's reference is acknowledged (e.g., liking a character's fanpage on Facebook), and the like. Using this method, the relativesalience of a character can be determined based on the number of times acharacter in a media content elicits reactions versus the number oftimes all characters in the media content elicit reactions. For example,this computation can be performed as:

${S({Char})} = \frac{{NumberOfReactions}({Char})}{{NumberOfReactions}({AllChars})}$

Yet another method to determine salience is to consider the character'sprevalence on the Internet in general. The prevalence can be determineda number of ways. On method is to identify the number of search resultsreturned from a reliable search engine for the name of a character. Forexample, searching Google for “Bill Clinton” returns about 40,400,000results. Searching Google for “George W. Bush” returns about 95,800,000results. Thus, the character George W. Bush is more salient with respectto the character Bill Clinton. Using this method, the prevalence of thecharacter on the Internet can be used to calculate salience in a similarmanner as the number of on-screen minutes for characters, as describedabove.

Another method for gathering either general or user specific charactersalience is to utilize physiological responses to characters. Forexample, eye-tracking data may be used to assess that on average viewersspend more time looking at Sheldon, Leonard, and Penny than any othercharacters on The Big Bang Theory, giving these characters particularlyhigh salience over the population. Alternatively, or in addition, thesystem may compute that a specific user, Jessica, spent the majority ofher time looking at Penny, indicating that Penny was the most salientcharacter to her. An additional way to gather user specific salience isby analyzing a user's behavior on social media in response to watching ashow.

One of ordinary skill in the art will readily recognize that not allcharacters of the media content must be considered for the saliencetechniques described above. For example, a minimum threshold value maybe set so that insignificant characters (e.g., those who receive verylittle screen time, those who elicit very few social media reactions,those who have low prevalence on the Internet, and the like) are notconsidered in the salience calculations. Alternatively, or in addition,a maximum threshold may also be set so that characters in a particularmedia content that are very popular do not overshadow other charactersin the salience calculations.

The consumers' preferences, the characters' attributes, and thecharacters' salience are considered for calculating a rating for a mediacontent. This rating can then be used to rank various media content andrecommend media content to consumers. Consider a consumer named Stevenwho is interested in viewing more females in television shows. Stevenhas indicated, or it has been inferred from his revealed preferences,that he would particularly like to see female scientists and that heprefers scientists in the television shows that he watches to beattractive. The system computes a character preference function for eachcharacter with relation to Steven's preferences to account for theseattributes. The character preference function represents a consumer'srating of the character. To compute the character preference functions,the salience of the characters is used. In this case, the salience foreach character is pre-computed and identified in Table 1, below, withrelation to the characters identified in FIG. 1. The salience values inthis case were calculated based on screen time for each character usinga particular episode of the show The Big Bang Theory. As discussedabove, other methods may be used. Additionally, the calculation may bebased on a single scene of a media content, a single episode of a mediacontent, a single season of a media content, or all the available showsof a media content.

TABLE 1 Exemplary Salience Values Character Salience Sheldon 0.2 Leonard0.2 Penny 0.2 Howard 0.15 Rajesh 0.15 Bernadette 0.05 Amy 0.05

Let cε

^(n) represent the attribute values for a character char on N distinctdimensions. The following character preference function is used tocalculate a rating for each character:

${f({Char})} = {{\sum\limits_{i = 1}^{N}\; {\beta_{i} \cdot c_{i}}} + {\sum\limits_{i,j}\; {\beta_{{i +},{j +}} \cdot c_{i +} \cdot c_{j +}}} + {\sum\limits_{i,j}{\beta_{{i -},{j -}} \cdot c_{i -} \cdot c_{j -}}} + {\sum\limits_{i,j}{\beta_{{i +},{j -}} \cdot c_{i +} \cdot c_{j -}}} + {\sum\limits_{i,j}{\beta_{{i -},{j +}} \cdot c_{i -} \cdot c_{j +}}}}$

Additionally, higher order terms may also be included. For example, thecharacter preference function can be extended to:

${f({Char})} = {{\sum\limits_{i = 1}^{N}\; {\beta_{i} \cdot c_{i}}} + {\sum\limits_{i,j}\; {\beta_{{i +},{j +}} \cdot c_{i +} \cdot c_{j +}}} + {\sum\limits_{i,j}{\beta_{{i -},{j -}} \cdot c_{i -} \cdot c_{j -}}} + {\sum\limits_{i,j}{\beta_{{i +},{j -}} \cdot c_{i +} \cdot c_{j -}}} + {\sum\limits_{i,j}{{\beta_{{i -},{j +}} \cdot c_{i -} \cdot c_{j +}}\mspace{14mu} {higher}\mspace{14mu} {order}\mspace{14mu} {terms}}}}$

The coefficients β are determined separately for each user to allow forpersonalized recommendations. For the preferences indicated by Steven,the following character preference function is used to calculate arating for each character:

ƒ(Char)=Gender+(Gender*Scientist)+(Attractiveness*Scientist)

Using the character models of FIG. 1 and Steven's character preferencefunction, the ratings of the characters illustrated in FIG. 1 arecomputed for Steven as follows:

ƒ(Sheldon)=−1+(−1×1)+(−1×1)=−3

ƒ(Leonard)=−1+(−1×1)+(0×1)=−2

ƒ(Penny)=1+(1×−1)+(1×−1)=−1

ƒ(Howard)=−1+(−1×1)+(−1×1)=−3

ƒ(Rajesh)=−1+(−1×1)+(0×1)=−1

ƒ(Bernadette)=1+(1×1)+(1×1)=3

ƒ(Amy)=1+(1×1)+(−1×1)=1

These calculated character ratings are valid for the charactersidentified for a particular episode of The Big Bang Theory. Thecalculated character ratings and their corresponding salience values canbe used to calculate a show rating for that particular episode of TheBig Bang Theory. The calculation of the show rating is performed bysumming the product of each character's salience and rating. Using asalience vector {right arrow over (S)} and characters rating vector{right arrow over (R)}, an episode rating R is calculated as:

R(Show_EpisodeX)={right arrow over (S)}·{right arrow over (R)}

In this particular example of Steven with relation to the charactersillustrated in FIG. 1, the rating R of The Big Bang Theory (TBBT)episode is calculated as:

(TBBT)=(0.2×−3)+(0.2×−2)+(0.2×−1)+(0.15×−3)+(0.15×−1)+(0.05×3)+(0.05×1)=−1.6

Thus, the rating for this particular episode of The Big Bang Theory forthe consumer Steven is −1.6. For recommendations, this rating value iscompared to similarly calculated rating values for other shows. Rankingsare prepared based on the rating values. For example, the media contentwith the highest rating values will be ranked highest while the mediacontent with the lowest rating values will be ranked the lowest. Thehighest ranked shows are recommended to the consumer, as these highlyranked shows represent the shows that the consumer is likely to beinterested in or is likely to enjoy. One of ordinary skill in the artwill appreciate that coefficients are a type of parameter, and that moregeneralized parameters for other functional forms may be used instead ofcoefficients.

System Integration

The techniques discussed above may be used separately or combined toproduce a powerful system for discovering and organizing characters andmedia content based on consumer preferences.

FIG. 2 illustrates an exemplary block diagram for a combined techniqueto perform discovery and organization of characters and media content.At block 202, the system accesses feedback from users. This feedback isused to determine the user's preferences and develop a characterpreference function. The feedback may be explicit, such as throughdirect questions. The feedback may be implicit, such as by analyzingwebpages the user has clicked on, viewed, commented on, shared, and thelike. The feedback may be physiological, such as by eye tracking,galvanic skin response, electroencephalography, facial expressiontracking, posture mapping, and the like. The character preferencefunction is stored in a database and associated with the user from whomthe feedback was received.

At block 204, attributes to be included in a character model aredetermined. Multiple examples are described. Physical attributes of thecharacters may be tracked, such as gender, age, and the like.Personality attributes may be tracked, such as kindness, humor, cruelty,and the like. Social attributes or roles may be tracked, such asrelationship (parent/grandparent), community leader, occupation, and thelike. For shorter form content, emotional and motivational states suchas excitement, anger, and/or hunger may be tracked. Additionalattributes may be tracked, such as race, socioeconomic class, and thelike.

At block 206, data relevant to the characters and their attributes areextracted from data sources. The data may be extracted from the text ofwebpages, such as Wikipedia, fan sites, social networks, such asFacebook and Twitter, surveys, expert validation, and other sources.

At block 208, character decomposition is performed. The data extractedfrom the data sources is used to assign values for each attributeidentified in block 204 for the characters in a character modeldatabase. For example, the various techniques described above may beused to assign values for each character's character model.

At block 210, the user preference model and the character model areaccessed to determine user preferences across the character attributesof the character model. The preference data is used to discover newcharacters or new shows that the user may like. The preference data mayalso be used to organize characters or shows based on the user'spreferences, such as identifying which characters are similar ordissimilar. As a result, the system is able to efficiently and reliablyrecommend characters and media content to the user.

FIG. 3 illustrates an exemplary process for recommending media. At block302, a recommendation system accesses a set of salience values. Thesalience values of the set are associated with a media content. Eachsalience value is associated with one character from the media content.The salience values are indicative of how important the characters areto the feel or tone of the show. The higher the salience value of acharacter, the more important the character. At block 304, the systemaccesses a character preference function. The character preferencefunction is associated with a user of the system. The characterpreference function comprises information that identifies a plurality ofpreference coefficients. Each of the preference coefficients in theplurality of preference coefficients is associated with at least oneattribute of interest, selected from a plurality of attributes. Forexample, the preference function may indicate that the user has apreference coefficient of 1 associated with a “gender” attribute ofinterest and a preference coefficient of 1 associated with a “scientist”attribute of interest.

At block 306, the system accesses a first character model. The firstcharacter model is associated with a first character from the mediacontent. The first character model includes information that identifiesa first set of attribute values. The attribute values are matched withattributes of the first character. The attributes may be the same as theattributes for which the character preference function includespreference coefficients. The first character is also associated with afirst salience value from the set of salience values. The first saliencevalue will be used to determine how much influence the first characterhas when computing a rating of the media content.

At block 308, the system accesses a second character model. The secondcharacter model is associated with a second character from the mediacontent. The second character model includes information that identifiesa second set of attribute values. The attribute values are matched withattributes of the second character. The attributes may be the same asthe attributes for which the character preference function includespreference coefficients. The second character is also associated with asecond salience value from the set of salience values. The secondsalience value will be used to determine how much influence the secondcharacter has when computing a rating of the media content.

At block 310, the system calculates a first character rating of thefirst character by performing a summation of the products of theplurality of preference coefficients with the first set of attributevalues. For example, the system will multiply the preference coefficientfor gender with the first character's attribute value for gender. Thesystem will also multiply the preference coefficient for scientist withthe first character's attribute value for scientist. These two productsfor gender and scientist are then added together. The first characterrating of the first character is based on this summation.

At block 312, the system similarly calculates a second character ratingof the second character by performing a summation of the products of theplurality of preference coefficients with the second set of attributevalues. For example, the system will multiply the preference coefficientfor gender with the second character's attribute value for gender. Thesystem will also multiply the preference coefficient for scientist withthe second character's attribute value for scientist. These two productsfor gender and scientist are then added together. The second characterrating of the second character is based on this summation.

At block 314, the system calculates a media content rating. The mediacontent rating is calculated based on the first salience value, secondsalience value, the first character rating, and the second characterrating. The salience values are used to weight the influence that eachcharacter rating has on the media content rating.

At block 316, the system recommends the media content to the user basedon the media content rating. The recommendation may be simply providingthe title of the media content, providing a link to the media content,displaying the media content, and the like. For example, the mediacontent may be an advertisement that the system has determined the usermay enjoy, connect with, or sympathize with. In other examples, themedia content may be a written article, a game, a mobile app or computerapplication, and the like.

In general, the blocks of FIG. 3 may be performed in various orders, andin some instances may be performed partially or fully in parallel.Additionally, not all blocks must be performed. For example, the set ofsalience values need not necessarily be accessed before accessing thefirst and second character models.

FIG. 4 illustrates an exemplary process for recommending media. At block402, a recommendation system calculates a first salience value of a setof salience values. The first salience value is associated with a firstcharacter of a plurality of characters of a media content. The firstsalience value is calculated based on the on-screen time of the firstcharacter in the media content. More specifically, the system determinesor accesses a total on-screen time value. For example, the totalon-screen time value may be sum of the time all (or select) charactersof the media content spend on screen. The system also determines oraccesses the on-screen time for the first character. The first saliencevalue is calculated by dividing the on-screen time of the firstcharacter by the total on-screen time value.

At block 404, the system calculates a second salience value of the setof salience values. The second salience value is associated with asecond character of the plurality of characters of the media content.The second salience value is calculated based on the on-screen time ofthe second character in the media content. More specifically, the systemdetermines or accesses the on-screen time for the second character. Thesecond salience value is calculated by dividing the on-screen time ofthe second character by the total on-screen time value.

The salience values of the set are associated with the media content.Each salience value is associated with one character from the mediacontent. The salience values are indicative of how important thecharacters are to the feel or tone of the show. The higher the saliencevalue of a character, the more important the character.

At block 406, the system accesses a character preference function. Thecharacter preference function is associated with a user of the system.The character preference function comprises information that identifiesa plurality of preference coefficients. Each of the preferencecoefficients in the plurality of preference coefficients is associatedwith at least one attribute of interest, selected from a plurality ofattributes. For example, the preference function may indicate that theuser has a preference coefficient of 0.8 associated with “femalescientist” attributes of interest, a preference coefficient of 1associated with a “female” attribute of interest, and a preferencecoefficient of 1 associated with a “scientist” attribute of interest.

This character preference function is a second order function. Thesecond order function has first order terms and second order terms. Thecharacter preference function associates at least one of the pluralityof preference coefficients with two or more attributes of interest ofthe plurality of attributes. In this example, the character preferencefunction associates the preference coefficient of 0.8 with theattributes of interest of “female scientist.”

At block 408, the system determines a first character model. The firstcharacter model is associated with the first character from the mediacontent. The first character model includes information that identifiesa first set of attribute values. The attribute values are matched withattributes of the first character. The attributes associated with theattribute values may be the same as the attributes for which thecharacter preference function includes preference coefficients.

The first character model is determined in part by identifying textualcontent associated with the first character in electronic sources, suchas websites, electronic books, electronic newspapers and magazines,social media, and the like. The system aggregates a plurality ofattribute terms associated with the first character from the textualcontent. For example, the system may aggregate terms such as “cute,”“smart,” “social,” and the like. The system maps at least some of theplurality of attribute terms to at least some of the plurality ofattributes. This mapping allows a relationship to be identified betweenthe aggregated terms (such as “cute”) and the attributes of thecharacter that are tracked (such as “attractive”). The system updatesthe attribute values of the first character based on the plurality ofattribute terms.

At block 410, the system calculates a first character rating of thefirst character. The system sums the first order terms and the secondorder terms of the character preference function in conjunction with thefirst character model. For the first order terms, the system calculatesthe products of the plurality of preference coefficients that are firstorder with the first set of attribute values. In this example, thesystem multiplies the preference coefficient of 1 associated with“scientist” with the first character model's attribute value for“scientist.” Similarly, the system multiplies the preference coefficientof 1 associated with “female” with the first character model's attributevalue for “female.” For the second order terms, the system determinesthe product of the at least one of the plurality of preferencecoefficients with each attribute value of the first set of attributevalues of the two or more attributes of interest of the plurality ofattributes. In order words, the system calculates the products of theplurality of preference coefficients that are second order with thefirst set of attribute values. In this example, the system multipliesthe preference coefficient of 0.8 associated with “female scientist”with the first character model's attribute value for “female” and withthe first character model's attribute value for “scientist.” The firstorder terms and second order terms are then summed to produce the firstcharacter rating.

Each of the attribute terms may be associated with a strength value.This is helpful for distinguishing between strong terms and less strongterms. For example, a strong term may indicate that a character is“definitely friendly.” A less strong term may indicate that thecharacter is “sometimes friendly.” The system then updates the attributevalues of the first character based on the corresponding strength valuesof the attribute terms. In this example, “definitely friendly” may beassociated with a 1.5 for the friendliness attribute, while “sometimesfriendly” is associated with a 0.75 for the friendliness attribute. Inone example, the system stores the updated attribute values of the firstcharacter in a database as a vector, the vector associated with thefirst character.

At block 412, the system determines a second character model. The secondcharacter model is associated with the second character from the mediacontent. The second character model includes information that identifiesa second set of attribute values. The attribute values are matched withattributes of the second character. The attributes associated with theattribute values may be the same as the attributes for which thecharacter preference function includes preference coefficients.

The second character model is determined in a similar fashion asdescribed above with respect to the first character model. The secondcharacter model is determined in part by identifying textual contentassociated with the second character in electronic sources. The systemaggregates a plurality of attribute terms associated with the secondcharacter from the textual content. The system maps at least some of theplurality of attribute terms to at least some of the plurality ofattributes. The system updates the attribute values of the firstcharacter based on the plurality of attribute terms and thecorresponding strength values of the attribute terms. In one example,the system stores the updated attribute values of the second characterin a database as a vector, the vector associated with the secondcharacter.

At block 414, the system calculates a second character rating of thesecond character. The second character rating is computed in a similarfashion as the first character rating. However, the second charactermodel and second character attribute values are used. At block 416, thesystem calculates a second character rating of the second character in asimilar fashion as calculated for the first character.

At block 416, the system calculates a media content rating. The mediacontent rating is calculated based on the first salience value, secondsalience value, the first character rating, and the second characterrating. The salience values are used to weight the influence that eachcharacter rating has on the media content rating.

At block 418, the system accesses a minimum content rating value. Atblock 420, the system compares the media content rating to the minimumcontent rating value. The media content rating is numerical and theminimum content rating value is numerical. If the media content ratingis greater than the minimum content rating value, the system moves toblock 422. Otherwise, the process ends at block 424.

At block 422, the system recommends the media content to the user basedon the media content rating. The recommendation may be simply providingthe title of the media content, providing a link to the media content,displaying the media content, and the like. For example, the mediacontent may be an advertisement that the system has determined the usermay enjoy, connect with, or sympathize with. In other examples, themedia content may be a written article, a game, a mobile app or computerapplication, and the like.

In general, the blocks of FIG. 4 may be performed in various orders, andin some instances may be performed partially or fully in parallel.Additionally, not all blocks must be performed. For example, the firstcharacter rating and the second character rating may be computed inparallel.

While FIG. 4 is described with respect to recommending media to a user,the techniques described above may be applied to various other systems.In one example, the system may be used to provide an interface to filterand curate information based on character decomposition. Morespecifically, the system may be used to: alter rankings for a list ofitems, control viewing abilities of a television or other viewingsystem, suggest content for purchasing, recommend video games or preventaccess to video games, filter content to prevent children from viewingcontent with a negative message, characterize what a child is watching,or find the intersection between the desires of a parent and the viewingpreferences of a child.

In another example, the system may provide information to contentproducers to understand audience preferences based on characterdecomposition, such as through use of character-based analytics. Morespecifically, the system may be used to: aggregate insights to contentproducers on what types of characters to create based on aggregated userdemand or preferences, or identify characteristics/attributes of acharacter most likely to resonate with a particular target user group toenable mapping of a character/celebrity with a target audience.

In another example, the system may provide complementary, simultaneous,character-based browsing and information discovery. More specifically,the system may be used to: provide a second screen experience, enhanceviewing experience of media with simultaneous recommendations, andprovide in-play ads based on characters appearing in the show.

In yet another example, the system may be used for user-generatedcharacter creation. Users can create their own characters based on whatfeatures the user likes. This allows for the collection ofuser-generated signals and data that informs the development ofcharacters based on attributes the user (or users) value most. This alsogenerates insights on user preferences and latent demand for specifictypes of characters as well as explicitly informing and directing newcharacter development for content providers.

Character-Based Analytics

Turning now to details and examples pertaining to character-basedanalytics for media content, a technique is described that providesorganization, indexing, and retrieval of character-level informationabout media content. Character-level information may also be used foranalytics, where outcome measures may include one or both ofmeasurable/computed events (e.g., viewing times, click-throughs, shares,or completions) and elicited measures from a user (e.g., star ratings ona video, or answers to survey questions such as attitude toward a brand,emotional responses to the video), in some instances these may includethe long term effects of media (for example, the potential effects ofexposure to some media on self-esteem). One example of an elicitedemotional outcome measure is rankings (e.g., on a scale from 1-7) ordata received in response to questions such as “Did you like thecontent?”, “Did the content make you angry?”, and “Did the content makeyou outraged?”

In one embodiment, the technique provides the ability to index media,such as videos and images used for entertainment or advertising,according to the attributes of the characters contained within the media(e.g., using character models). For example, a set of media may be usedfor user entertainment (e.g., movies), advertising purposes (e.g.,advertisements), or both. For one or more media in the in the set ofmedia (or for each media in the set of media), the system performs oneor more of the following: (a) index the characters, either by name oranother identifier (e.g., identify how many characters there are anduniquely identify the characters), (b) index the characters on one ormore objective and/or subjective attributes, such as based on thecharacter models discussed above, (c) retrieve information (e.g.,character model of a character) from the index of characters, and (d)retrieve information (e.g., a list of characters or a character model ofa character) from the index of characters based on character attributes.

For example, information may be retrieved from the index of charactersby performing one or more of the following: (a) identifying theidentifiers for one or more (or optionally, all) characters in a media,(b) accessing the values for one or more attributes of each the one ormore characters in the media, (c) aggregating the attributes and/orvalues for the one or more attributes over a plurality of the one ormore characters in the media to obtain attribute-level values for each(or at least one) of the one or more attributes using any of thefollowing exemplary capture functions: (1) maximum attribute value overall characters (e.g., how attractive is the most attractive character inthe media, how intelligent is the most intelligent character in themedia), (2) maximum attribute value over all characters within ademographic category (e.g. how attractive is the most attractive male inthe media, how intelligent is the most intelligent Latina in the media),(3) average attribute value over characters within a demographiccategory (e.g., what is the average attractiveness of male characters inthe media, what is the average intelligence of Latino women in themedia), and/or (4) a salience weighted average value (e.g., what is theweighted average attractiveness of male characters in the media, withthe salience and weights being specified as described above).

For another example of retrieving items from the index of charactersbased on character attributes, all items are retrieved from the index ofcharacters for characters that are: “confident AND female” or “black ANDyoung AND male”.

In another embodiment, a technique provides the ability to analyze howattributes of characters within media content correspond to outcomemeasures. Outcome measures may be either predetermined, computed, orelicited as described above.

One example of a computed outcome measure includes calculating thenumber (or rate) of user interactions (e.g., website click-through,playback completion, exceeding a viewing duration) with a media content(e.g., advertisement). The user interaction (e.g., outcome measureinformation) may be a click-through or a click-through rate by one ormore users on a webpage or other interactive user interface. Theclick-through rate may be an aggregate or may be specific to aparticular demographic. In other examples, the user interaction may be aviewing time of the media content (e.g., video, webpage, or image), suchas an advertisement. In another example, the user interaction may bebased on a determination of whether a timed media (such as a video) iswatched, listened to, played back, or perceived by a user to completion(or for at least a threshold period of time). This interaction (e.g.,outcome measure information) may be recorded as a binary variablespecifying whether the user interaction with the media lasted untilcompletion of the playback of the media content. Similarly, theinteraction (e.g., outcome measure information) may be recorded as abinary variable specifying whether the user interaction with the medialasted for at least a threshold period of time. The threshold period oftime may be, for example, 15 seconds, 20 seconds, 25 seconds, or 30seconds. Exceeding one or more of these threshold periods of time forinteraction by a user indicates the user's interest in the media. Theuser interaction may also be a measure of time the user spendsinteracting (e.g., listening, watching) with the media content. Thus,the measure of time for interaction may be in seconds, minutes, hours,or the like. Another example of a computed outcome measure includescalculating the number (or rate) of postings (e.g., outcome measureinformation) to social media.

In some examples, computed outputs may be computed based on the use of awebsite browser cookie, may be computed and provided by a third party,or may be combination of the two. For example, the technique may monitormedia interactions (such as views) by users, but receive demographicinformation of those users from a system of a third-party provider.Accordingly, in this example, calculating the views by members of aparticular demographic is based on a combination of monitoring userinteractions with media and demographic data provided by the system ofthe third-party provider. In other examples, a single system computes(e.g., calculates, measures, or determines) both the media interactionsand the user demographic information.

In one exemplary predictive analytics embodiment, a car company has fivedifferent Internet video advertisements in circulation for a new car.For example, the advertisements are being displayed to Internet users ofa forum website. Each of the five video advertisements features one ormore different respective spokespersons (e.g., one or more characters)who convey information about the new car to the viewer of theadvertisement, such as through a sales pitch explaining the benefits ofthe car. The car company wants to measure the relative effectiveness ofeach of the five video advertisements (or the effectiveness of eachspokesperson) with a specific demographic (e.g., a demographic ofinterest: young men, such as men between the ages of 18-35). The systemcollects outcome measure information (e.g., user's interaction withcontent), such as completion rates, for the advertisements for a largesample of users (e.g., between 1,000-2,000 users). The system alsocollects demographics information for the sample of users. For example,the system may collect and store information that relates a particularuser's viewing session, and therefore advertisement interactioninformation, with the demographics information of the user. For example,the demographics information of the user may have been received andstored by the system at the time the user registered for an account withthe forum website. In some embodiments, the identity of the useroptionally remains anonymous to the system. The system then uses thedemographics information (e.g., accesses demographics information of aplurality of users to identify a subset of the plurality of users) andthe interaction information to determine which attributes of thespokespersons (e.g., a character in a media) are most effective (e.g.,result in high completion rates) for that specific demographic (e.g.,the demographic of interest: men between the ages of 18-35).

In one example of this determination, the system accesses charactermodels for one or more (or for each) character of one or more (or each)of the five advertisements. For each respective advertisement, thesystem identifies a value of the confidence attribute of the mostconfident female character (e.g., assume 0 if there is no femalecharacter), a value of the confidence attribute of the most confidentmale character (e.g., assume 0 if there is no male character), a valueof the attractiveness attribute of the most attractive female character(e.g., assume 0 if there is no female character), and a value of theattractiveness attribute of the most attractive male character (e.g.,assume 0 if there is no male character) in the respective advertisement.This technique illustrates the above-described capture function using amaximum value of a character attribute within a demographic ofcharacters.

This yields four potential predictor variables for the outcome measuresof interest: (1) confidence of the female character, (2) confidence ofthe male character, (3) attractiveness of the female character, and (4)attractiveness of the male character. Exemplary data for the fiveadvertisements with respect to the predictor variables is illustrated intable 600 of FIG. 6.

Table 600 illustrates values for different attributes of characters foreach of the five advertisements, where each row of table 600 is for oneof the five advertisements. In this example, column 602 represents avalue of the confidence attribute of the most confident femalecharacter. Thus, if advertisement 1 includes two female characters, afirst female character with a confidence value of 6 and a second femalecharacter with a confidence value of 8, the system will identify thehighest confidence value of the two female characters (i.e., 8), asillustrated in cell 602A. A similar technique is followed for the other4 advertisements and for each of the other three attributes, asillustrated in rows 604, 606, and 608. For example, the attribute valuesof the characters may be on a scale from 0 to 10.

Alternatively, or in addition, capture functions other than identifyinga maximum within a demographic may be used for determining the values ofattributes for the media (e.g., the five advertisements), as describedabove. For example, other functions include: maximum attribute valueover all characters, average attribute value over all characters, and asalience weighted average value for characters.

The system also calculates an outcome measure based on the interactioninformation. In this example, the interaction information includeswhether an advertisement was viewed to completion (e.g., did the userwatch the entire video advertisement or stop/end the video advertisementprior to completion) and the outcome measure is based on the rate ofwhether the advertisement was viewed to completion. The outcome measuremay be limited to the specific demographic (e.g., the demographic ofinterest: men between the ages of 18-35). Exemplary computed outcomemeasures for the five advertisements are illustrated in table 650 ofFIG. 6.

Table 650 illustrates completion rates for each of the fiveadvertisements, where each row of table 650 is for one of the fiveadvertisements. In this example, column 652 represents the rate ofcompletion for each of the five advertisements, where a value of 1implies every user of the specific demographic watched the advertisementto completion and 0 implies no user of the specific demographic watchedthe advertisement to completion. Thus, cell 652A illustrates thatapproximately 1.2% of the specific demographic (e.g., the demographic ofinterest: men between the ages of 18-35) watched advertisement 1 tocompletion.

The system uses the information of table 600 and table 650 to perform aregression. Regression analysis generally generates an equation todescribe the statistical relationship between the one or more predictorvariables and the response variable. The regression may be, for example,a linear regression (e.g., where the dependent variable is continuous)or a logistic regression (e.g., where the dependent variable is discreteor categorical). The regression coefficients generally represent themean change in the response variable for one unit of change in thepredictor variable, while holding other predictors in the modelconstant. Accordingly, the regression helps to isolate the role of onepredictor variable from all of the other predictor variables in themodel.

In this example, performing the regression determines that for thespecific demographic (e.g., the demographic of interest: men between theages of 18-35), the significant coefficients for watching advertisementsto completion are on “Confidence of female” (e.g., column 604 of table600) and “Attractiveness of Male” (e.g., column 608 of table 600)attributes. For example, the system may use a threshold value for thestatistical significance level or absolute value of coefficients todetermine which attributes are significant. Similarly, performing theregression determines that the “Attractiveness of Female” and“Confidence of Male” attributes are not significant predictors of thespecific demographic (e.g., the demographic of interest: men between theages of 18-35) watching advertisements to completion. This significanceinformation may be stored in the system (or remotely) for subsequentretrieval.

Based on this calculated information and determination, the advertisercan choose to target advertisements with confident female charactersand/or attractive male characters to the specific demographic (e.g., thedemographic of interest: men between the ages of 18-35). For example,the system can determine demographic information for a new user of theforum website, and select an advertisement for display from among aplurality of advertisements, wherein the advertisement for display isselected based on the demographic information of the new user and thestored (or calculated) significance information. Accordingly, the systemmay display advertisement 2 or 4. The system may select advertisement 2or 4 by determining which advertisements exceed a minimum thresholdvalue for one or both of the attributes of significance. In thisexample, the minimum threshold value may be 5 for both “Confidence ofFemale” and “Attractiveness of Male”. Thus, advertisements 2 and 4 meetthe minimum threshold value. In some examples, a first attribute ofsignificance is assigned a first minimum threshold value and a secondattribute of significance is assigned a second minimum threshold value,where the first and second minimum threshold values are different.Similarly, additional attributes determined to be significant may beassigned different corresponding minimum threshold values for selectionof the advertisement for display.

FIG. 7 illustrates an exemplary process for selecting media content fordisplay. At block 702, a system accesses demographics information of aplurality of users to identify a subset of the plurality of users andoutcome measure information of the subset of the plurality of users, theoutcome measure information and other potential outcome measuresrelating to a plurality of media content, the plurality of media contentcomprising a first media content and a second media content.

At block 704, the system calculates a first outcome measure (or set ofoutcome measures) for the first media content, the first outcome measurebased on the outcome measure information, and a second outcome measure(or set of outcome measures) for the second media content, the secondoutcome measure based on the outcome measure information.

At block 706, the system accesses respective character models of one ormore characters depicted in the first media content and respectivecharacter models of one or more characters depicted in the second mediacontent.

At block 708, the system determines, for the first media content, afirst attribute value of a first attribute of the one or more charactersdepicted in the first media content, the determination based on therespective character models and in accordance with a first capturefunction, and a second attribute value of a second attribute of the oneor more characters depicted in the first media content, thedetermination based on the respective character models and in accordancewith a second capture function. The system also determines, for thesecond media content, a third attribute value of the first attribute ofthe one or more characters depicted in the second media content, thedetermination based on the respective character models and in accordancewith the capture function, and a fourth attribute value of the secondattribute of the one or more characters depicted in the second mediacontent, the determination based on the respective character models andin accordance with the capture function.

At block 710, the system performs a regression using the first attributevalue, the second attribute value, the third attribute value, the fourthattribute value, the first outcome measure (or set of outcome measures),and the second outcome measure (or set of outcome measures) to determineat least one attribute of significance.

In some embodiments, the at least one attribute of significance isdetermined based on a value of the at least one attribute ofsignificance exceeding a threshold significance value. In someembodiments, the outcome measure information comprises video playbackcompletion data for the first media content and the second media contentand wherein the first attribute is different from the second attribute.

In some embodiments, the outcome measure information comprises minimumduration of video playback data for the first media content and thesecond media content. In some embodiments, the determined attribute ofsignificance is one of the first attribute and the second attribute.

In some embodiments, the outcome measure information comprises anelicited measure of the user's emotional response to the first mediacontent and the second media content.

In some embodiments, the plurality of media content further comprises athird media content. The system calculates a third outcome measure (orset of outcome measures) for the third media content, the third outcomemeasure based on the outcome measure information, and accessesrespective character models of one or more characters depicted in thethird media content. The system determines, for the third media content,a fifth attribute value of the first attribute of the one or morecharacters depicted in the third media content, the determination basedon the respective character models and in accordance with the firstcapture function, and a sixth attribute value of the second attribute ofthe one or more characters depicted in the third media content, thedetermination based on the respective character models and in accordancewith the second capture function. Performing the regression to determinethe at least one attribute of significance further comprises using thefifth attribute value and the sixth attribute value.

At block 712, in some embodiments, the system selects media content fordisplay, the media selected based on depicting a character having atleast a threshold value of the at least one attribute of significance.Alternatively, or in addition, the system may compose and/or display ananalytics dashboard. The analytics dashboard may optionally include oneor more of: the outcome measures (or set of outcome measures) describedabove, statistics (and/or images) based on the outcome measuresdescribed above, one or more of the attributes of significance describedabove, statistics (and/or images) based on the attribute(s) ofsignificance described above, the attribute values, images based on theattribute values, the capture functions, further descriptive analyticsdescribed below, data visualizations or presentations of the preceding,and other insights generated as a result of the analysis.

In some embodiments, the system may determine which attributes arepredictive of particular outcomes and the degree to which the attributesare predictive of the particular outcomes. The system may select asubset of the attributes for display based on the subset of theattributes being significant predictors of the particular outcomes. Thesystem may display the selected subset of attributes and the degree towhich the attributes are predictive of the particular outcome.

In some embodiments, the system selects (and/or displays) attributes ofinterest that are significant predictors of the outcome measure.

Descriptive analytics examples are now described. Consider anadvertising platform or policy-maker seeking to understand whether women(e.g., a first value for an attribute of interest: female) and men(e.g., a second attribute for the attribute of interest: male) areportrayed differently in advertisements (or media, generally) for aparticular company or for a particular industry.

The system performs the following analysis to determine whether (or towhat degree) characters of the advertisements are portrayed equally fora set of relevant attributes. The system retrieves character models formale characters and female characters of the advertisements. The systemcomputes the total number of female characters and the total number ofmale characters in the advertisements. The system also computes andcompares the average values for one or more of the set of relevantattributes (e.g., intelligence, confidence, and attractiveness).

If the system determines significant differences (e.g., exceeding adetermined threshold) in the number of males or females representationor in one or more of the relevant attributes (e.g., these may includeimportant motivations such as the desire for leadership), the system maygenerate a notification, such as for display in the advertising platformor for notification of the policy maker. This may, for example, promptthe policy-maker to encourage or require the industry to include morecharacters of the under-represented gender in advertisements or make aneffort to portray characters of both genders more equally on at leastone of the set of relevant attributes. Continuing this example, theanalysis may show that female characters are not only shown less, butare shown to have significant less desire for leadership. A policy makermay subsequently create a set of recommendations or guidelines toincrease the number of female characters shown in leadership roles andas desiring leadership.

The system may also perform this type of descriptive analytics analysison advertisements targeted at a specific demographic (e.g., youngwomen), on advertisements in a particular industry, or on advertisementstargeted to a particular demographic.

In one embodiment, character models may be used to analyze characterdriven engagement (e.g., interaction) with media. Specifically thesystem relies on character attributes as the basis for analyzing andpredicting user engagement, where engagement may be one or more of (1)viewership of a tv show or movie (e.g. Nielsen Rating, box office draws,or more direct measures such as views on a particular streaming videowebsite), (2) social media engagement with either the media (e.g., tvshow) in general or a particular character in a media, such as byincluding comments about the show or character, and “likes,” “votes,” orother light-weight actions pertaining to the show or character, and (3)deeper engagement with the character or show including generating usercontent (e.g., fan fiction, video tributes, and parodies) pertaining toa tv show or movie. These engagement measures can either be general overthe entire viewing public or restricted to specific demographic orpsychographic groups (e.g. men ages 18-35 or “millennial women”).

Predictors of engagement can be determined using character models. Thesystem determines a dependent variable of interest (e.g. comments onsocial media about each character of interest, number of video tributesto each of these characters, or number of fan fiction stories centeredaround each of these characters). The dependent variable of interest isthe measure of engagement: y, where y_(i) is the measured engagementlevel for the i^(th) character.

The system constructs dependent variables of interest using thecharacter models. In one example, the dependent variables of interestincludes the character model itself (e.g. the character's attributevalues on one or more attributes), or can be expanded to include higherorder interaction terms (e.g. (gender==female)*leadership, or(gender==male)*(race==Asian)*confident). These dependent variables maybe represented in a matrix X, where the elements x_(ij) represent thevalue the i^(th) character on the j^(th) dependent variable.

The system estimates the parameters, β of a function F(X,β)=y. In oneexample, F(X,β)=X·β. In another example, F(X,β) is equal to anytransformation of g(X·β), where g is a monotonic function on

$\left( {{{e.g.\mspace{14mu} {g(x)}} = \sqrt{x}},{{{or}\mspace{14mu} {g(x)}} = \frac{^{x}}{1 + ^{x}}}} \right).$

The system determines the predictors of engagement using the parametersβ.

In one embodiment, character models may be used to determine predictorsof engagement for media (e.g., a tv show, movie, or advertisement). Adependent variable of interest is identified, as described above. Thevariable of interest may be based on characteristic of media, ratherthan an attribute of a character (e.g. viewership of a show or movie asmeasured by Nielsen or Box Office draw, comments on social media boutthe content, or user-generated content about the media content. Thedependent variable is the measure of engagement: y, where y_(i) is themeasured engagement level for i^(th) piece of content.

The system constructs independent variables of interest using thecharacter models for one or more characters in each media (e.g., tvshow). This can be done several ways. In one example, the systemcalculates a salience weighted mean value of an attribute of one or morecharacters in the show (e.g., the average intelligence of characters inthe show). Note that for categorical features such as race or genderthis will yield the proportion of characters with that attribute (e.g.the proportion of female characters or the proportion of whitecharacters). In another example, the system calculates the maximum orminimum of one or more attributes of one or more characters in the show(e.g., how smart is the smartest character on the show, or how dumb isthe dumbest character on the show). In another example, the systemcalculates using an aggregate of character attributes using mean,salience weighted mean, maximum or minimum (e.g., how intelligent is thesmartest woman on the show, or what is the mean intelligence of women onthe show). These content-level independent variables can be stored in amatrix X, where the elements x_(ij) represent the value the i^(th) showon the j^(th) independent variable.

The system calculates (e.g., estimates) the parameters β of a functionF(X,β)=y. In one example, F(X, β)=X·β. In another example, F(X,β) equalsany transformation of g(X·β), where g is a monotonic function on

(e.g.,

$\left. {{{g(x)} = \sqrt{x}},{{{or}\mspace{14mu} {g(x)}} = \frac{^{x}}{1 + ^{x}}}} \right).$

The system determines the predictors of engagement based on theparameters β.

One example of vector-based searching to identify characters or mediacontent is now described. In FIG. 8, at block 802, the system accesses adatabase, the database including a plurality of character identificationvalues associated with a plurality of character models, the plurality ofcharacter models comprising a set of attribute values for a plurality ofattributes.

At block 804, the system receives a search request, the search requestcomprising a first attribute search threshold value associated with afirst attribute of the plurality of attributes and a second attributesearch threshold value associated with a second attribute of theplurality of attributes.

At block 806, the system selects a subset of the characteridentification values based on character models associated with thesubset of the character identification values meeting the firstattribute search threshold value for the first attribute and meeting thesecond attribute search threshold value for the second threshold.

In some embodiments, at block 808, the system displays a listing ofcharacters identified by the subset of the character identificationvalues.

In some embodiments, determines plurality of media content based on theplurality of media content including one or more of the charactersidentified by the subset of the character identification values. Thesystem displays a listing of the plurality of media content.

In some embodiments, the system alters rankings of a list of mediacontent based on the selected subset of character identification values.

In some embodiments, prevents a display device from displaying contenton the selected subset of character identification values.

In some embodiments, recommends media content for purchase based on theselected subset of character identification values.

FIG. 5 depicts an exemplary computing system 500 configured to performany one of the above-described processes. In this context, computingsystem 500 may include, for example, a processor, memory, storage, andinput/output devices (e.g., monitor, keyboard, touch screen, disk drive,Internet connection, etc.). However, computing system 500 may includecircuitry or other specialized hardware for carrying out some or allaspects of the processes. In some operational settings, computing system500 may be configured as a system that includes one or more units, eachof which is configured to carry out some aspects of the processes eitherin software, hardware, or some combination thereof.

FIG. 5 depicts computing system 500 with a number of components that maybe used to perform the above-described processes. The main system 502includes a motherboard 504 having an input/output (“I/O”) section 506,one or more central processing units (“CPU”) 508, and a memory section510, which may have a flash memory device 512 related to it. The I/Osection 506 is connected to a display 524, a keyboard 514, a diskstorage unit 516, and a media drive unit 518. The media drive unit 518can read/write a computer-readable medium 520, which can containprograms 522 and/or data. The I/O section 506 may also connect to cloudstorage using, for example, cellular data communications or wirelesslocal area network communications.

At least some values based on the results of the above-describedprocesses can be saved for subsequent use. Additionally, anon-transitory computer-readable medium can be used to store (e.g.,tangibly embody) one or more computer programs for performing any one ofthe above-described processes by means of a computer. The computerprogram may be written, for example, in a general-purpose programminglanguage (e.g., Perl, C, C++, Java) or some specializedapplication-specific language.

Although only certain exemplary embodiments have been described indetail above, those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiments withoutmaterially departing from the novel teachings and advantages of thesetechniques. For example, aspects of embodiments disclosed above can becombined in other combinations to form additional embodiments.Similarly, aspects of embodiments disclosed above can be excluded.Accordingly, all such modifications are intended to be included withinthe scope of this disclosure.

1-23. (canceled)
 24. A computer-implemented method for searching formedia content or characters, the method comprising: accessing adatabase, the database including a plurality of character identificationvalues associated with a plurality of character models, the plurality ofcharacter models comprising a set of attribute values for a plurality ofattributes; receiving a search request, the search request comprising afirst attribute search threshold value associated with a first attributeof the plurality of attributes and a second attribute search thresholdvalue associated with a second attribute of the plurality of attributes;and selecting a subset of the character identification values based oncharacter models associated with the subset of the characteridentification values meeting the first attribute search threshold valuefor the first attribute and meeting the second attribute searchthreshold value for the second threshold.
 25. The computer-implementedmethod of claim 24, the method further comprising: displaying a listingof characters identified by the subset of the character identificationvalues.
 26. The computer-implemented method of claim 24, the methodfurther comprising: determining a plurality of media content based onthe plurality of media content including one or more of the charactersidentified by the subset of the character identification values; anddisplaying a listing of the plurality of media content.
 27. Thecomputer-implemented method of claim 24, the method further comprising:altering rankings of a list of media content based on the selectedsubset of character identification values.
 28. The computer-implementedmethod of claim 24, the method further comprising: preventing a displaydevice from displaying content on the selected subset of characteridentification values.
 29. The computer-implemented method of claim 24,the method further comprising: recommending media content for purchasebased on the selected subset of character identification values.
 30. Anon-transitory computer-readable storage medium comprisingcomputer-executable instructions for searching for media content orcharacters, the computer-executable instructions comprising instructionsfor: accessing a database, the database including a plurality ofcharacter identification values associated with a plurality of charactermodels, the plurality of character models comprising a set of attributevalues for a plurality of attributes; receiving a search request, thesearch request comprising a first attribute search threshold valueassociated with a first attribute of the plurality of attributes and asecond attribute search threshold value associated with a secondattribute of the plurality of attributes; and selecting a subset of thecharacter identification values based on character models associatedwith the subset of the character identification values meeting the firstattribute search threshold value for the first attribute and meeting thesecond attribute search threshold value for the second threshold. 31.The non-transitory computer-readable storage medium of claim 30, furthercomprising computer-executable instructions for: displaying a listing ofcharacters identified by the subset of the character identificationvalues.
 32. The non-transitory computer-readable storage medium of claim30, further comprising computer-executable instructions for: determininga plurality of media content based on the plurality of media contentincluding one or more of the characters identified by the subset of thecharacter identification values; and displaying a listing of theplurality of media content.
 33. The non-transitory computer-readablestorage medium of claim 30, further comprising computer-executableinstructions for: altering rankings of a list of media content based onthe selected subset of character identification values.
 34. Thenon-transitory computer-readable storage medium of claim 30, furthercomprising computer-executable instructions for: preventing a displaydevice from displaying content on the selected subset of characteridentification values.
 35. The non-transitory computer-readable storagemedium of claim 30, further comprising computer-executable instructionsfor: recommending media content for purchase based on the selectedsubset of character identification values.
 36. An apparatus forsearching for media content or characters, the apparatus comprising:memory; and one or more computer processors configured to: access adatabase, the database including a plurality of character identificationvalues associated with a plurality of character models, the plurality ofcharacter models comprising a set of attribute values for a plurality ofattributes; receive a search request, the search request comprising afirst attribute search threshold value associated with a first attributeof the plurality of attributes and a second attribute search thresholdvalue associated with a second attribute of the plurality of attributes;and select a subset of the character identification values based oncharacter models associated with the subset of the characteridentification values meeting the first attribute search threshold valuefor the first attribute and meeting the second attribute searchthreshold value for the second threshold.
 37. The apparatus of claim 36,the one or more computer processors further configured to: display alisting of characters identified by the subset of the characteridentification values.
 38. The apparatus of claim 36, the one or morecomputer processors further configured to: determine a plurality ofmedia content based on the plurality of media content including one ormore of the characters identified by the subset of the characteridentification values; and display a listing of the plurality of mediacontent.
 39. The apparatus of claim 36, the one or more computerprocessors further configured to: alter rankings of a list of mediacontent based on the selected subset of character identification values.40. The apparatus of claim 36, the one or more computer processorsfurther configured to: prevent a display device from displaying contenton the selected subset of character identification values.
 41. Theapparatus of claim 36, the one or more computer processors furtherconfigured to: recommend media content for purchase based on theselected subset of character identification values.